A Novel Hybrid Linear Predictive Coding � Discrete Cosine Transform based Compression

Abstract

Image compression is a type of data compression applied to digital images, for reducing the cost for their storage and transmission. Algorithms may take advantage of Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless. On useful technique used for lossy compression is the discrete cosine transform (DCT) that helps separate the image into parts (or spectral sub-bands) of differing importance (with respect to the image's visual quality). The DCT is similar to the discrete Fourier transform in the sense that it transforms a signal or image from the spatial domain to the frequency domain. This paper proposes a hybrid lossy compression technique using Linear Predictive Coding (LPC) and Discrete Cosine Transform (DCT) to provide superior compression ratios.